Assignment1_TOD

I. Introduction & Motivation

II. Setup: Set parameters and load libraries

Load Libraries

knitr::opts_chunk$set(echo = TRUE)
options(scipen=999)


library(tidyverse)

## ── Attaching packages ─────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

## ── Conflicts ────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(tidycensus)
library(sf)

## Linking to GEOS 3.8.1, GDAL 3.1.1, PROJ 6.3.1

library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

library(grid)
library(knitr)
library(kableExtra)

## 
## Attaching package: 'kableExtra'

## The following object is masked from 'package:dplyr':
## 
##     group_rows

library(rmarkdown)

Load Styling Options

mapTheme <- function(base_size = 12) {
  theme(
    text = element_text( color = "black"),
    plot.title = element_text(size = 16,colour = "black"),
    plot.subtitle=element_text(face="italic"),
    plot.caption=element_text(hjust=0),
    axis.ticks = element_blank(),
    panel.background = element_blank(),axis.title = element_blank(),
    axis.text = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_rect(colour = "black", fill=NA, size=2),
    strip.text.x = element_text(size = 14))
}

plotTheme <- function(base_size = 12) {
  theme(
    text = element_text( color = "black"),
    plot.title = element_text(size = 16,colour = "black"),
    plot.subtitle = element_text(face="italic"),
    plot.caption = element_text(hjust=0),
    axis.ticks = element_blank(),
    panel.background = element_blank(),
    panel.grid.major = element_line("grey80", size = 0.1),
    panel.grid.minor = element_blank(),
    panel.border = element_rect(colour = "black", fill=NA, size=2),
    strip.background = element_rect(fill = "grey80", color = "white"),
    strip.text = element_text(size=12),
    axis.title = element_text(size=12),
    axis.text = element_text(size=10),
    plot.background = element_blank(),
    legend.background = element_blank(),
    legend.title = element_text(colour = "black", face = "italic"),
    legend.text = element_text(colour = "black", face = "italic"),
    strip.text.x = element_text(size = 14)
  )
}

Functions Preparation

qBr <- function(df, variable, rnd) {
  if (missing(rnd)) {
    as.character(quantile(round(df[[variable]],0),
                          c(.01,.2,.4,.6,.8), na.rm=T))
  } else if (rnd == FALSE | rnd == F) {
    as.character(formatC(quantile(df[[variable]]), digits = 3),
                 c(.01,.2,.4,.6,.8), na.rm=T)
  }
}

q5 <- function(variable) {as.factor(ntile(variable, 5))}


multipleRingBuffer <- function(inputPolygon, maxDistance, interval) 
{
  #create a list of distances that we'll iterate through to create each ring
  distances <- seq(0, maxDistance, interval)
  #we'll start with the second value in that list - the first is '0'
  distancesCounter <- 2
  #total number of rings we're going to create
  numberOfRings <- floor(maxDistance / interval)
  #a counter of number of rings
  numberOfRingsCounter <- 1
  #initialize an otuput data frame (that is not an sf)
  allRings <- data.frame()
  
  #while number of rings  counteris less than the specified nubmer of rings
  while (numberOfRingsCounter <= numberOfRings) 
  {
    #if we're interested in a negative buffer and this is the first buffer
    #(ie. not distance = '0' in the distances list)
    if(distances[distancesCounter] < 0 & distancesCounter == 2)
    {
      #buffer the input by the first distance
      buffer1 <- st_buffer(inputPolygon, distances[distancesCounter])
      #different that buffer from the input polygon to get the first ring
      buffer1_ <- st_difference(inputPolygon, buffer1)
      #cast this sf as a polygon geometry type
      thisRing <- st_cast(buffer1_, "POLYGON")
      #take the last column which is 'geometry'
      thisRing <- as.data.frame(thisRing[,ncol(thisRing)])
      #add a new field, 'distance' so we know how far the distance is for a give ring
      thisRing$distance <- distances[distancesCounter]
    }
    
    
    #otherwise, if this is the second or more ring (and a negative buffer)
    else if(distances[distancesCounter] < 0 & distancesCounter > 2) 
    {
      #buffer by a specific distance
      buffer1 <- st_buffer(inputPolygon, distances[distancesCounter])
      #create the next smallest buffer
      buffer2 <- st_buffer(inputPolygon, distances[distancesCounter-1])
      #This can then be used to difference out a buffer running from 660 to 1320
      #This works because differencing 1320ft by 660ft = a buffer between 660 & 1320.
      #bc the area after 660ft in buffer2 = NA.
      thisRing <- st_difference(buffer2,buffer1)
      #cast as apolygon
      thisRing <- st_cast(thisRing, "POLYGON")
      #get the last field
      thisRing <- as.data.frame(thisRing$geometry)
      #create the distance field
      thisRing$distance <- distances[distancesCounter]
    }
    
    #Otherwise, if its a positive buffer
    else 
    {
      #Create a positive buffer
      buffer1 <- st_buffer(inputPolygon, distances[distancesCounter])
      #create a positive buffer that is one distance smaller. So if its the first buffer
      #distance, buffer1_ will = 0. 
      buffer1_ <- st_buffer(inputPolygon, distances[distancesCounter-1])
      #difference the two buffers
      thisRing <- st_difference(buffer1,buffer1_)
      #cast as a polygon
      thisRing <- st_cast(thisRing, "POLYGON")
      #geometry column as a data frame
      thisRing <- as.data.frame(thisRing[,ncol(thisRing)])
      #add teh distance
      thisRing$distance <- distances[distancesCounter]
    }  
    
    #rbind this ring to the rest of the rings
    allRings <- rbind(allRings, thisRing)
    #iterate the distance counter
    distancesCounter <- distancesCounter + 1
    #iterate the number of rings counter
    numberOfRingsCounter <- numberOfRingsCounter + 1
  }
  
  #convert the allRings data frame to an sf data frame
  allRings <- st_as_sf(allRings)
}

other setup

palette5 <- c("#CCCCFF","#CC99FF","#9966CC","#663399","#330066")

# Load census API key

census_api_key("a7edab3d7c3df571998caab5a3cc12a4ec8d8b61" , overwrite  = TRUE)

## To install your API key for use in future sessions, run this function with `install = TRUE`.

III. Data manipulation and visualization

1. Data Wrangling

Census Data

# View(load_variables(2009,'acs5',cache = TRUE))

tracts09 <-  
  get_acs(geography = "tract", variables = c("B25026_001E","B02001_002E","B15001_050E",
                                             "B15001_009E","B19013_001E","B25058_001E",
                                             "B06012_002E"), 
          year=2009, state= 48, county= 113, geometry=T) %>% 
  st_transform('EPSG:32138')

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# Let's "spread" the data into wide form
# each row is tract, each column is variable
tracts09 <- 
  tracts09 %>%
  dplyr::select( -NAME, -moe) %>%
  spread(variable, estimate) %>%
  dplyr::select(-geometry) %>%
  rename(TotalPop = B25026_001, 
         Whites = B02001_002,
         FemaleBachelors = B15001_050, 
         MaleBachelors = B15001_009,
         MedHHInc = B19013_001, 
         MedRent = B25058_001,
         TotalPoverty = B06012_002)

# st_drop_geometry drops the geometry of its argument, and reclasses it accordingly
st_drop_geometry(tracts09)[1:3,]

## # A tibble: 3 x 8
##   GEOID Whites TotalPoverty MaleBachelors FemaleBachelors MedHHInc TotalPop
##   <chr>  <dbl>        <dbl>         <dbl>           <dbl>    <dbl>    <dbl>
## 1 4811…   3621          311            11              54    77036     4269
## 2 4811…   3146          140            11              10    92212     3435
## 3 4811…   3529          163             0              41    80195     3880
## # … with 1 more variable: MedRent <dbl>

# Let's create new rate variables using mutate

tracts09 <- 
  tracts09 %>%
  mutate(pctWhite = ifelse(TotalPop > 0, Whites / TotalPop, 0),
         pctBachelors = ifelse(TotalPop > 0, ((FemaleBachelors + MaleBachelors) / TotalPop), 0),
         pctPoverty = ifelse(TotalPop > 0, TotalPoverty / TotalPop, 0),
         year = "2009") %>%
  dplyr::select(-Whites,-FemaleBachelors,-MaleBachelors,-TotalPoverty)


# Tracts 2009 is now complete. Let's grab 2017 tracts and do a congruent
# set of operations

# ---- 2017 Census Data -----

# Notice that we are getting "wide" data here in the first place
# This saves us the trouble of using "spread"

tracts17 <- 
  get_acs(geography = "tract", variables = c("B25026_001E","B02001_002E","B15001_050E",
                                             "B15001_009E","B19013_001E","B25058_001E",
                                             "B06012_002E"), 
          year=2017, state= 48, county= 113, geometry=T, output="wide") %>%
  st_transform('EPSG:32138') %>%
  rename(TotalPop = B25026_001E, 
         Whites = B02001_002E,
         FemaleBachelors = B15001_050E, 
         MaleBachelors = B15001_009E,
         MedHHInc = B19013_001E, 
         MedRent = B25058_001E,
         TotalPoverty = B06012_002E) %>%
  dplyr::select(-NAME, -starts_with("B")) %>% #-starts_with("B") awesome!
  mutate(pctWhite = ifelse(TotalPop > 0, Whites / TotalPop,0),
         pctBachelors = ifelse(TotalPop > 0, ((FemaleBachelors + MaleBachelors) / TotalPop),0),
         pctPoverty = ifelse(TotalPop > 0, TotalPoverty / TotalPop, 0),
         year = "2017") %>%
  dplyr::select(-Whites, -FemaleBachelors, -MaleBachelors, -TotalPoverty) 

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# --- Combining 09 and 17 data ----

allTracts <- rbind(tracts09,tracts17)

Transit Data

## Reading layer `PassengerRail' from data source `https://opendata.arcgis.com/datasets/65b609c50cba44c19cfbaa2a0251a8ae_0.geojson' using driver `GeoJSON'
## Simple feature collection with 74 features and 4 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: -97.32795 ymin: 32.64762 xmax: -96.56221 ymax: 33.03387
## geographic CRS: WGS 84

Create Buffers

2. TOD vs. Non-TOD: Four Variables

3. Grouped Bar Plot

4. Comparison Table

Variable	2009: Non-TOD	2009: TOD	2017: Non-TOD	2017: TOD
Median_Household_Income	57573.74	47845.36	65220.27	59105.64
Percent_Bach	0.01	0.01	0.01	0.02
Percent_Poverty	0.15	0.21	0.16	0.20
Percent_White	0.61	0.61	0.61	0.66
Population	5101.41	4479.06	5186.17	4255.57
Rent	756.44	706.39	947.34	921.06
Table 2.3

5. Graduated Symbol Maps of Population and Rent within 0.5 mile of each transit station

selected_stops <- 
  st_transform(D_Stops,('EPSG:32138')) %>%
  st_union(st_buffer(D_Stops, 2640)) %>%
  st_sf() %>%
  dplyr::select(STA_NAME)

# 1. Graduated symbol map of population within 0.5 mile of each transit station
join_data <- 
  st_join(tracts17, selected_stops) %>%
  filter(!is.na(STA_NAME)) 

popByStation <-
  join_data %>% 
  group_by(STA_NAME) %>%
  summarize(sumPop = sum(TotalPop)) %>%
  dplyr::select(STA_NAME, sumPop) %>%
  st_drop_geometry() %>%
  left_join(D_Stops)

popByStation_sf <- popByStation %>%
  st_sf()
  
Dallas <- st_read('https://www.dallasopendata.com/api/geospatial/mn9w-m2tp?method=export&format=GeoJSON')

## Reading layer `OGRGeoJSON' from data source `https://www.dallasopendata.com/api/geospatial/mn9w-m2tp?method=export&format=GeoJSON' using driver `GeoJSON'
## Simple feature collection with 3 features and 3 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -97.00049 ymin: 32.61333 xmax: -96.46367 ymax: 33.02382
## geographic CRS: WGS 84

ggplot() + 
  geom_sf(data = Dallas, fill = 'grey') +
  geom_sf(data = popByStation_sf, aes(size = sumPop), color = 'blue') +
  labs(title = "Graduated Symbol Map: Population within 0.5 Mile of Each Transit Station" ,
       subtitle = '',
       caption = "Figure 5.1", 
       fill = 'Total Population') +
  plotTheme() + 
  theme(legend.position="right") + 
  theme(plot.title = element_text(size=15)) 

# 2. Graduated symbol maps of median rent within 0.5 mile of each transit station.
rentByStation <-
  join_data %>%
  group_by(STA_NAME) %>%
  summarize(median_Rent = median(na.omit(MedRent))) %>%
  dplyr::select(STA_NAME, median_Rent)  %>% 
  st_drop_geometry() %>%
  left_join(D_Stops)

rentByStation_sf <- rentByStation %>%
  st_sf()

ggplot() + 
  geom_sf(data = Dallas, fill = 'grey') +
  geom_sf(data = rentByStation_sf, aes(size = median_Rent), color = 'purple')  +  
  labs(title = "Graduated Symbol Map: Median Rent within 0.5 Mile of Each Transit Station",
       subtitle = '',
       caption = "Figure 5.2",
       fill = 'Median Rent') +
  plotTheme() + 
  theme(legend.position="right") + 
  theme(plot.title = element_text(color = "darkred", size=15, face="bold"))

6. Rent as a function of distance to subway stations

7. Crime, Transit Access and Rents

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IV. Conclusion